26,432 research outputs found
Learned Perceptual Image Enhancement
Learning a typical image enhancement pipeline involves minimization of a loss
function between enhanced and reference images. While L1 and L2 losses are
perhaps the most widely used functions for this purpose, they do not
necessarily lead to perceptually compelling results. In this paper, we show
that adding a learned no-reference image quality metric to the loss can
significantly improve enhancement operators. This metric is implemented using a
CNN (convolutional neural network) trained on a large-scale dataset labelled
with aesthetic preferences of human raters. This loss allows us to conveniently
perform back-propagation in our learning framework to simultaneously optimize
for similarity to a given ground truth reference and perceptual quality. This
perceptual loss is only used to train parameters of image processing operators,
and does not impose any extra complexity at inference time. Our experiments
demonstrate that this loss can be effective for tuning a variety of operators
such as local tone mapping and dehazing
Photographic image enhancement
Deblurring capabilities would significantly improve the scientific return from Space Shuttle crew-acquired images of the Earth and the safety of Space Shuttle missions. Deblurring techniques were developed and demonstrated on two digitized images that were blurred in different ways. The first was blurred by a Gaussian blurring function analogous to that caused by atmospheric turbulence, while the second was blurred by improper focussing. It was demonstrated, in both cases, that the nature of the blurring (Gaussian and Airy) and the appropriate parameters could be obtained from the Fourier transformation of their images. The difficulties posed by the presence of noise necessitated special consideration. It was demonstrated that a modified Wiener frequency filter judiciously constructed to avoid over emphasis of frequency regions dominated by noise resulted in substantially improved images. Several important areas of future research were identified. Two areas of particular promise are the extraction of blurring information directly from the spatial images and improved noise abatement form investigations of select spatial regions and the elimination of spike noise
Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization
Image enhancement aims at processing an input
image so that the visual content of the output image is more
pleasing or more useful for certain applications. Although
histogram equalization is widely used in image enhancement due
to its simplicity and effectiveness, it changes the mean brightness
of the enhanced image and introduces a high level of noise and
distortion. To address these problems, this paper proposes
image enhancement using fuzzy intensity measure and adaptive
clipping histogram equalization (FIMHE). FIMHE uses fuzzy
intensity measure to first segment the histogram of the original
image, and then clip the histogram adaptively in order to
prevent excessive image enhancement. Experiments on the
Berkeley database and CVF-UGR-Image database show that
FIMHE outperforms state-of-the-art histogram equalization
based methods
Aesthetic-Driven Image Enhancement by Adversarial Learning
We introduce EnhanceGAN, an adversarial learning based model that performs
automatic image enhancement. Traditional image enhancement frameworks typically
involve training models in a fully-supervised manner, which require expensive
annotations in the form of aligned image pairs. In contrast to these
approaches, our proposed EnhanceGAN only requires weak supervision (binary
labels on image aesthetic quality) and is able to learn enhancement operators
for the task of aesthetic-based image enhancement. In particular, we show the
effectiveness of a piecewise color enhancement module trained with weak
supervision, and extend the proposed EnhanceGAN framework to learning a deep
filtering-based aesthetic enhancer. The full differentiability of our image
enhancement operators enables the training of EnhanceGAN in an end-to-end
manner. We further demonstrate the capability of EnhanceGAN in learning
aesthetic-based image cropping without any groundtruth cropping pairs. Our
weakly-supervised EnhanceGAN reports competitive quantitative results on
aesthetic-based color enhancement as well as automatic image cropping, and a
user study confirms that our image enhancement results are on par with or even
preferred over professional enhancement
Image Enhancement with Statistical Estimation
Contrast enhancement is an important area of research for the image analysis.
Over the decade, the researcher worked on this domain to develop an efficient
and adequate algorithm. The proposed method will enhance the contrast of image
using Binarization method with the help of Maximum Likelihood Estimation (MLE).
The paper aims to enhance the image contrast of bimodal and multi-modal images.
The proposed methodology use to collect mathematical information retrieves from
the image. In this paper, we are using binarization method that generates the
desired histogram by separating image nodes. It generates the enhanced image
using histogram specification with binarization method. The proposed method has
showed an improvement in the image contrast enhancement compare with the other
image.Comment: 9 pages,6 figures; ISSN:0975-5578 (Online); 0975-5934 (Print
Digital TV image enhancement system
Efficient, digital image-enhancement process has been developed for high-resolution slow-scan TV images. Scan converter is no longer subject to registration errors, which become more serious as resolution increases. To implement feedback image enhancement system, digital processing is used; otherwise there is excessive loss of image information, particularly in video delay lines
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